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#' Import data from a spatial RDD into a Spark Dataframe.
#'
#' @description Import data from a spatial RDD (possibly with non-spatial attributes) into a
#' Spark Dataframe.
#' * `sdf_register`: method for sparklyr's sdf_register to handle Spatial RDD
#' * `as.spark.dataframe`: lower level function with more fine-grained control on non-spatial columns
#'
#' @param x A spatial RDD.
#' @param name Name to assign to the resulting Spark temporary view. If
#'   unspecified, then a random name will be assigned.
#' @param non_spatial_cols Column names for non-spatial attributes in the
#'   resulting Spark Dataframe. By default (NULL) it will import all field names if that property exists, in particular for shapefiles.
#'
#'
#' @importFrom sparklyr sdf_register
#'
#' @return A Spark Dataframe containing the imported spatial data.
#'
#' @examples
#' library(sparklyr)
#' library(apache.sedona)
#'
#' sc <- spark_connect(master = "spark://HOST:PORT")
#'
#' if (!inherits(sc, "test_connection")) {
#'   input_location <- "/dev/null" # replace it with the path to your input file
#'   rdd <- sedona_read_geojson_to_typed_rdd(
#'     sc,
#'     location = input_location,
#'     type = "polygon"
#'   )
#'   sdf <- sdf_register(rdd)
#'
#'   input_location <- "/dev/null" # replace it with the path to your input file
#'   rdd <- sedona_read_dsv_to_typed_rdd(
#'     sc,
#'     location = input_location,
#'     delimiter = ",",
#'     type = "point",
#'     first_spatial_col_index = 1L,
#'     repartition = 5
#'   )
#'   sdf <- as.spark.dataframe(rdd, non_spatial_cols = c("attr1", "attr2"))
#' }
#'
#' @export
sdf_register.spatial_rdd <- function(x, name = NULL) {
  as.spark.dataframe(x, name = name)
}


#' @export
#' @rdname sdf_register.spatial_rdd
as.spark.dataframe <- function(x, non_spatial_cols = NULL, name = NULL) {
  sc <- spark_connection(x$.jobj)

  # Default keep all columns
  if (is.null(non_spatial_cols)) {
    if (!is.null(invoke(x$.jobj, "%>%", list("fieldNames")))) { ## Only if dataset has field names
      non_spatial_cols <- invoke(x$.jobj, "%>%", list("fieldNames"), list("toString")) ### Get columns names
      non_spatial_cols <- gsub("(^\\[|\\]$)", "", non_spatial_cols)  ##### remove brackets
      non_spatial_cols <- strsplit(non_spatial_cols, ", ")[[1]]  ##### turn into list
    }
  } else {
    stopifnot("non_spatial_cols needs to be a character vector (or NULL, default)" = is.character(non_spatial_cols))
  }

  sdf <- invoke_static(
    sc,
    "org.apache.sedona.sql.utils.Adapter",
    "toDf",
    x$.jobj,
    as.list(non_spatial_cols),
    spark_session(sc)
  )
  sdf_register(sdf, name)
}
